Data assimilation for predicting the dynamics of acute respiratory infections using the ensemble Kalman filter - Yolanda Norasia; Dinni Rahma Oktaviani; Aini Fitriyah; Devi Marita Putri (Universitas Ahmad Dahlan, 2026)
Acute respiratory infection (ARI) is one of the most pressing public health problems due to its high transmission rate and the potential to cause significant pressure on health services. This study applies the Ensemble Kalman Filter (EnKF) method to predict the spread of ARI with a three-compartment population model, namely Susceptible (S), Exposed (E), and Infected (I). This study shows that the EnKF method can predict the spread of ARI well. The number of ensembles used affects the level of accuracy. The EnKF provides accurate predictions of the dynamics of ARI spread, making it relevant as a scientific basis in the formulation of data-based mitigation strategies. It can provide a scientific basis for policymakers to formulate accurate and measurable preventive measures.